Teaching

I design and teach engineering courses focused on systems thinking, experimentation, and applied AI development.

My teaching experience spans since 2005, evolving from assistant roles to leading undergraduate and graduate-level courses in electrical engineering, computer science, and AI systems.

Across institutions, my work integrates laboratory-based learning, research-oriented projects, and real-world engineering systems.

Teaching Summary

• Teaching experience: 2005 – Present
• Course leadership: 2015 – Present
• 70+ course instances delivered
• 2500+ students taught
• Undergraduate and graduate-level instruction
• Thesis supervision in AI, computer vision, and biomedical engineering

Core Teaching Systems

My teaching is organized around two primary engineering systems:

• Laboratory-based engineering education
• AI and data-driven system design education

Laboratory-Based Engineering Courses

These courses focus on experimental systems, instrumentation, and real-world constraints.

EL3201 – Electrical Engineering Laboratory

Universidad de Chile (FCFM)
Instructor (100%) · 2022–2026

System focus

Transition from theoretical electrical engineering to experimental system design under real constraints.

Teaching emphasis

• Experimental design and validation
• Instrumentation and measurement systems
• Reproducibility in engineering experiments
• Technical communication of results

Student outputs

• Student project presentations

AI and Computational Systems Courses

These courses focus on machine learning, robotics, and full AI system pipelines.

EL5206 – Computational Intelligence & Robotics Laboratory

Universidad de Chile (FCFM)
Co-instructor (with Martín Adams) · 2021–2026

System focus

End-to-end design of AI-driven systems combining:

• machine learning
• computer vision
• robotics integration

Teaching emphasis

• Data → model → system pipeline design
• Experimental validation of AI systems
• Integration of perception and decision-making modules
• Real-world constraint handling

Student outputs

• Final project presentations

Additional University Teaching Experience

Universidad Santo Tomás

Recent (2025-2026)

• Electricidad y Magnetismo
• Estática y Dinámica
• Ondas, Óptica y Calor
• Mecánica y Resistencia de Materiales

Previous (2017–2022)

• Cálculo Diferencial e Integral
• Matemáticas para Ingeniería
• Ecuaciones Diferenciales
• Electricidad y Magnetismo

Universidad Andrés Bello

Recent (2026)

• Matemáticas I
• Matemáticas III

Previous (2015–2019)

• Álgebra
• Cálculo
• Introducción a las Matemáticas

Universidad del Desarrollo (UDD)

• Deep Learning (Master in Data Science)

Early Teaching – Universidad de Chile

Electrical Engineering Department (DIE)

• Digital Systems Laboratory (2011–2019)
• Signals and Systems (2012–2017)
• Digital Image Processing (2011–2017)
• Digital Systems (2011–2013)
• Information Processing Systems (2010)
• Electronics Laboratory (2009–2011)

Mathematical Engineering Department (DIM)

• Linear Algebra (2007)
• Numerical Calculus (2005–2007)
• Numerical Calculus Laboratory (2005–2007)

Student Supervision & Research Mentorship

I supervise and evaluate undergraduate and graduate research projects in AI, computer vision, and biomedical systems.

My supervision approach focuses on:

problem formulation → system design → implementation → evaluation → communication

Thesis Supervision (Selected)

Advisor – Universidad de Chile

• Computer vision systems for sperm morphology analysis (2026, ongoing)
• Transformer-based vehicle identification systems (2026)
• AI-based glaucoma diagnosis systems (2024)
• Cancer imaging classification with vision transformers (2023)

Co-Advisor – Universidad de Chile

• AI-based monitoring of calving processes (2026, ongoing)
• Deep learning sperm segmentation systems (2025)

Committee Member

• Imaging AI systems (2023–2025)
• Industrial computer vision systems (2023)

External Evaluator – Tech4Medics Lab

Evaluation of applied AI systems in clinical environments:

• medical imaging AI
• diagnostic support systems
• biomedical signal analysis

Internship Supervision (SCIANLab)

• biomedical image segmentation systems
• transformer-based vision models
• embryo and cellular imaging systems
• low-data learning scenarios in medical imaging

Teaching Focus

My teaching is structured around:

• Laboratory-based experimentation
• Systems thinking in engineering
• End-to-end AI pipeline design
• Real-world constraint modeling
• Scientific and technical communication

Teaching Philosophy

Engineering education is an iterative process of building, testing, and reasoning under constraints.

My focus is on helping students develop the ability to:

• design systems, not just models
• validate ideas experimentally
• communicate technical reasoning clearly
• connect theory with real-world applications

Teaching is integrated with research and applied AI systems, ensuring that students engage with contemporary engineering problems.